| Oat β-glucan is a homogeneous linear polysaccharide,composed of Dglucopyranose and linked by mixing β-1,3 and β-1,4 bonds.It has wide applications in the field of food,and is the major component making oat more soluble and possess the function of hypoglycemic and hypolipidemic.Currently,the quantitative methods for oatβ-glucan face the disadvantages of being expensive,cumbersome,and time-consuming.Therefore,it’s necessary for the oat industry to develop accurate,rapid,and economical determination methods.In this study,three oat β-glucan determination methods were studied and developed,by using solvent retention capacity,near-infrared spectroscopy technology,and fluorescent whitening agent-Calcofluor,respectively.The enzymatic method(assay kit method)was regarded as a standard method.This study will help providing guidance for oat processing,novel products development,and breed selection.The results were as follows:(1)Unlike wheat flour’s solvent retention capacities(SRC),the SRCs of four conventional solvents and calcium chloride solvents was not significantly correlated with the protein,starch and lipid in oat,whereas these SRCs were significantly correlated with oat β-glucan content(p<0.05).And it was highly significantly correlated with calcium chloride SRC(r=0.615,p<0.01).The molecular weight of 30 oat β-glucan was significantly correlated with calcium chloride SRC(r=0.366,p<0.05),and the molecular weight was highly significantly correlated with water SRC(r=0.735,p<0.01).Multivariate stepwise regression analysis was performed to establish the prediction equations for oat β-glucan content and molecular weight.(2)Oat β-glucan content prediction model was established based on near-infrared spectroscopy technology and neural network.The near-infrared original spectrum was pretreated with smooth,standard multivariate scattering correction,and first derivative processing.The Mahalanobis distance was used to eliminate abnormal samples,and the principal component analysis was used to simplify the near-infrared spectrum information.A total of 12 factors were extracted with an accumulated explanation of 99%spectral information.A neural network model was used to establish oat β-glucan prediction model,and the model structure was[12,7,1].The samples were randomly divided into training set(70%),validation set(15%),and test set(15%).The overall determination coefficient of the model was 0.858.(3)The effects of ionic strength,starch and protein content in samples,and molecular weight of β-glucan on fluorescence detection were studied,and the precision and accuracy of detection of β-glucan by fluorescence method were evaluated.As the ionic strength increased,its fluorescence intensity increased as well.Oat starch and protein have no prominent effect on the fluorescence intensity,and the addition of oat protein caused a slight decrease in fluorescence intensity.The molecular weight of β-glucan had a slight effect on fluorescence intensity.Lower molecular weight induced a slightly decreased fluorescence intensity.The fluorescence method had high accuracy and precision in measuring the content of oat β-glucan.As molecular weight increased,the number of Calcofluor molecules bound to each β-glucan increased.(4)These three determination methods were compared with a standard of enzymatic method(assay kit method).The β-glucan contents of 8 different oat varieties were detected by using these three methods.Fluorescence method and enzymatic method had the highest correlation(r=0.996,p<0.01).Whereas the correlation between SRC method and the enzymatic method was the lowest(r=0.796,p<0.01).According to the testing steps and efficiency of these three methods,SRC method can be applied for preliminary screening of oat flour β-glucan content,near-infrared spectroscopy method is suitable for detection of oat samples on a large scale,and fluorescence method can be used for tests that require high accuracy. |
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